dr. javad rezaeian

In this research, a proposed integer non-linear programming model investigates the order planning problem in a multi-product, multi-period environment. The model focuses on unrelated parallel machine scheduling using make-to-stock (MTS)1 and make-to-order (MTO)2 combinational policies towards the just-in-time (JIT)3 approach. The objective function is to minimize total operating costs, procurement of raw materials, storage of raw materials and final products, tardiness, and rejection of orders. The proposed model is coded, solved, and validated using an illustrative example with Lingo 9.0 software. The results show the model's correct performance, which responds to most orders at appropriate times with the lowest inventory level. Also, considering the theoretical challenges and industrial applications, a genetic algorithm is proposed to solve this problem. The performance of the proposed algorithm is evaluated using several numerical issues. Finally, the analysis of computational results indicates the satisfactory performance of the algorithm.

The innovative self-collection service in urban logistics is proposed as an alternative to door-to-door delivery, offering environmental and social benefits. This research aims to investigate the level of awareness among the population regarding the new self-collection service for order deliveries by preparing a questionnaire and conducting interviews with residents of Tehran. It also aims to identify the factors influencing customers' choice of this service and to examine the relationships between these factors using the fuzzy DANP approach. Ultimately, the research seeks to present a mathematical model for covering self-collection centers within an urban logistics network, aiming to minimize the distance traveled by consumers. The study's findings indicate that the awareness of the self-collection service among the population in Iran is very low. However, there is a significant increase in the population's interest in this service when the collection points are located nearby, demonstrating the high potential of this innovative initiative. Furthermore, data analysis reveals that the criteria of 1) proximity of self-collection locations to customers, 2) cost differences between door-to-door delivery and self-collection services, and 3) the quality of services provided at these centers play a crucial role in advancing this new initiative. Finally, the research recommends public awareness and education about the environmental and economic benefits of this new service, mapping consumer purchasing habits, identifying strategic points, providing financial incentives and discounts, and enhancing service quality to increase customer acceptance of self-collection services, which will lead to improved performance of the urban logistics network.

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.

Modern organizations’ strategy is reducing inventory, production cycle time, transportation cost, quality cost, while increasing competitiveness to maximize its benefits. Supply chain management, as part of an organization’s strategy, involves several stakeholders with different interests, each controlling part of the chain and pursuing different goals. This paper presents a comprehensive multi-level model of a supply chain consisting of several simultaneous stakeholders in a systematic way. The developed model first, consider the various transportation factors, such as distribution of goods for various members of the supply chain to achieves the lowest transportation cost at different levels, for both transporting parts and products. The model also provides lowest costs of purchase and production by considering the costs of maintenance, supply shortages and shortage, for all the involved parties. In addition, a competition in the context of a game to select the optimal situation between the stakeholders of the supply chain has been considered to make the best decision in terms of a participatory game by considering different scenarios. Through applying the principles of the game theory, various scenarios were developed to optimize the benefits of parties and to achieve the optimal design. The results of a numerical example showed that the answer provided by the game theory for the value of the objective function was less than the answers of the transport problem, despite the deficit penalty. Therefore, it can be concluded that game theory can achieve a better answer by considering the conditions of alliance between participants in a collaborative game.

One of the most important problems for distribution companies (especially large online stores) is the design of docking stations, vehicle routing, and supply chain optimization. In this research, the distribution centers of Digi kala online store have been located. A multi-objective location-routing problem has been developed by meta-heuristic methods considering the principle of stability and time constraints. Based on the results of the proposed model, the effect of demand on the three objectives of time, cost, and environmental and social issues was investigated and it was shown that if demand increases, there will be a greater effect on time. In other words, increasing demand can create gaps that result in increased delivery time, and therefore this can lead to customer dissatisfaction in the long run. Increasing employment also leads to better environmental issues, but has no effect on cost or time. Also, the cost of transporting goods has no effect on environmental issues and time, but leads to increased costs.

Nowadays, identifying the risks of manufacturing industries is an effective factor in improving the performance of the health supply chain. This study aimed to identify and evaluate the risks of the manufacturing sector in the livestock and poultry feed supply chain in Golestan Province, Iran. For this purpose, first, the risks related to livestock and poultry feed industrial units were identified using the Failure Mode and Effects Analysis (FMEA) and then, by designing the Risk Breakdown Structure (RBS) approach, risk identification process was structured, which increased the comprehensiveness of the identification phase in terms of covering the features and characteristics of the project. Moreover, by using the fuzzy best-worst model (F BWM) and the Fuzzy DEMATEL (F DEMATEL) method, the weights and internal relationships of the criteria were calculated, respectively. For weighting and final prioritization, a combination of the two methods of F BWM and F DEMATEL was used. The present study was applied and descriptive in terms of purpose and nature, respectively. The data was both quantitative and qualitative, and library and field methods were used for data collection. According to the research results, among the risks of the production sector in the livestock and poultry feed supply chain of Golestan Province, the risk of the COVID-19 pandemic and the risk of increasing poverty were the highest and the lowest priorities, respectively.

Game theory, a branch of applied mathematics, which has been recently used to study strategic behavior, i.e. the conditions in which the desirability of each factor depends on the decisions made by the players. The paper proposes a multilevel green transportation model under multi-stakeholder’s condition, that game theory could be used to selects the best interactive mode by presenting different scenarios so that all stakeholders in a game achieve their maximum benefits.In this paper, a comprehensive multi-level supply chain model consisting of several simultaneous stakeholders has been considered. An optimal model was developed to achieve the lowest cost at different levels of the chain. The optimal model includes the lowest costs of purchase and production, maintenance, shortage, and warehousing. The model also includes various components of products transportation and distribution for all members of the chain. The produced Co2 at different stages of production and transportation for both involved parties was accounted in the model to minimize the environmental impacts. Furthermore, a competition in the context of a game to select the optimal situation between the stakeholders of the supply chain has been considered make the best decision in terms of a participatory game by considering different scenarios. In this paper, Excel and Gams software were used to model and solve the numerical example. The results confirmed the superiority of the optimal values of the game theory model.

Sequencing and scheduling of production in jobshop production systems was investigated in this article. Each operation specific to a job has a random duration with a mean and variance, taking into account this uncertainty in the assumptions of the model, the adaptation of the model to the real conditions of the production environment. Each job has the operating costs of the machine during processing, the cost of equipment idle for each unit of time delay in receiving the work. In this study, the optimal scheduling is Reduction of costs. The algorithm used to solve the problem is a genetic algorithm. The efficiency of the proposed algorithms has been tested and analyzed with a number of selected problems from the literature. This study was performed in a situation where the time of operation is uncertain and follows a specific statistical distribution (normal, exponential and uniform). The performance of the genetic algorithm was evaluated based on time criteria and objective function. The results obtained from the genetic algorithm were compared with the results obtained from the combined algorithm (neural network and SA algorithm) and the results obtained from the optimal solving procedures using Lingo software version 6 for 5 sample production scheduling problems. The results show that the integrated algorithm performed better than the genetic algorithm

Integrating production scheduling and scheduling is one of the most important decisions in any production organization that leads to increase the effectiveness of operations management. Long-term production planning decisions (determination of production quantity, inventory level, etc.) and short-term operational scheduling decisions (order of work processing and maintenance activities) have a direct effect on each other. In this study, a new mathematical model for simultaneous decision making of production planning and scheduling is presented with the aim of minimizing the total costs, time and delay in the hybrid flowshop. The concept of imperfect maintenance and considering several types of repair maintenance sources is presented in the proposed model. Two multi-objective meta-heuristic evolutionary algorithms based on the harmony search algorithm and the exact Epsilon-constraint solution method are proposed to solve the proposed model, which continuously searches the decision-making solution space. For this purpose, a sample of test problems of literature review and three performance criteria of Pareto number of solutions, mean ideal distance and Spacing metric are used to compare the results of meta-heuristic algorithms. The results show the advantages and effectiveness of the methods used in determining the Pareto optimal solution for optimization problems.

One of the important issues in supply chain management, vehicle routing and scheduling problem has always been which has so far attracted considerable attention from researchers. This paper attempts to optimize routing and scheduling for a heterogeneous transportation system with considering stochastic supply and demand, various traffic conditions, maximum continuous driving time constraint and soft time window constraint of each customer delivery. The consideration of pollution in routing decisions gives rise to a new routing framework where measures of the environmental implications are traded off with business performance measures. Also, the aim of this study is to minimize energy consumption and to maximize customer satisfaction, simultaneously. At first, this problem is presented in the form of a multi-objective linear programming model and then a novel integrated approach is proposed based on combination of chance constraint method and goal programming. Finally, the results of numerical example are analyzed with sensitivity analysis.
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The aim of this study was to present a model for promoting social capital in the Ministry of Energy and in two stages of designing a conceptual framework for research and validation of a conceptual framework. The first part of the statistical population of the study was professors, managers and experts in the field of social capital, of which 11 people were selected as a sample. The second part of the statistical population, including all employees, was selected from 244 samples. In order to collect information, the Delphi questionnaire was used and after identifying the components, indicators and criteria, a questionnaire with a 5-point Likert scale was used to confirm them. The validity of the questionnaire was evaluated using CVR index and exploratory and confirmatory factor analysis and its reliability was evaluated using Cronbach's alpha. Data analysis was performed using Delphi technique, one-sample t-test and structural equation modeling in SPSS and Lisrel software. Conflict of interest: According to authors, the article didn’t have any  Conflict of interest.

Appropriate scheduling of shifts of nurses for a hospital is a critical issue in hospital management. Researcher in this study improves the scheduling of shifts of nurses in health services organizations to provide lower cost and also reduce the computational complexity and finally realize the outcome of these actions in the form of job satisfaction and quality of received services. For problem discussed offered a linear mathematical model and after point to NP-hard this problem, Provided Genetic Algorithm for Solving the size was in fact and finally Implemented in Imam Khomeini Noor city as a real sample and proposed computational results and performance of the proposed algorithm in terms of solution quality and computational time, were analyzed. In the proposed model of accreditation standards for hospitals as well as the Productivity laws in the modeling is used. Production timetables by the proposed model, resulting in improved satisfaction levels of nurses and improve their job performance.

Using sensitivity analysis is one of the effective methods to simplify numerical models. There are a lot of parameters in models constructed for the simulation of oilfields. These parameters do not have the same impact on the model›s output. Therefore, utilization of the sensitivity analysis methods help that nonsignificant variables can be identified and kept at nominal values in order to save time and cost. According to studies, favorable method in sensitivity analysis is a dimensionless, quantitative, global, and model-free method. In this study, Sobol method has been selected as a desirable method for sensitivity analysis of production parameters. This method is applied to the surface model of one of Iran›s oilfields and it is characterized that parameters such as choke size, flowline diameter and pump frequency have the greatest effect on the oil production rate.

One of the most important aspects of human resource management is the allocation of the workforce to activities. Human resource assignment to project activities for its scheduling is one of the most real and common issues in project management and scheduling. This becomes even more significant when human resource assignment to multiple projects simultaneously is considered. On the one hand, workforces can have multi skills due to technological and scientific development so that they can be assigned to project activities based on their skill level. On the other hand, the learning effect is also taken into account to make the model more realistic. These factors can affect completion time, total cost and execution quality of projects. In this study, a multi-objective optimization model for multi-project scheduling and multi-skilled human resource assignment problem based on the learning effect and activities' quality is presented. A mixed-integer linear programming model (MILP) is developed for the proposed problem and solved by the ε-constraint method in GAMS software. Managers can select a solution based on their priority. Finally, a sensitivity analysis is done on the learning and forgetting effect to investigate their impacts on each objective function.

Sustainable supply chain networks have attracted considerable attention in recent years as a means of dealing with a broad range of environmental and social issues. This paper reports a multi-objective mixed-integer linear programming (MILP) model for use in the design of a sustainable closed loop supply chain network under uncertain conditions. The proposed model aims to minimize total cost, optimize environmental impacts of establishment of facilities, processing and transportation between each level as well as social impacts including customer satisfaction. Due to changes in business environment the uncertainty existed in the research problem, in this paper the chance constrained fuzzy programming approach applied to cope with uncertainties in parameter of the proposed model. Then the proposed multi-objective model solves as single-objective model using LP-metric method.

Green buildings are consistant objects which consider environment issues and natural resources throughout the life cycle. The risk of a green construction project reduces the accuracy of the estimation of the projects objectives and reduces the efficiency of green building projects. Therefore, identification and prioritization of risks can play a significant role in the success of green construction projects. In this research, we present a comprehensive model of all criteria and indicators of the risks of green construction projects; we have made the final ranking of green building construction risk indicators so that we can reduce the risk of green projects at run-time. For this purpose , at the first step, the identification and screening of indicators from the viewpoint of experts in green construction projects is considered by using the Fuzzy Delphi technique and then, the new method is used to combine the results of the network analysis process and the DEMATEL technique in fuzzy conditions to rank and evaluate the causal relationships between the factors and ultimately final prioritizing green building risk indicators have been done in terms of the results of the initial steps using the fuzzy Analysis Network Process (FANP) and DEMATEL technoique. The results of the research show that the risks associated with poor quality of materials and inappropriate equipment are very important and the lack of scale for the cost of activities has the least importance.

Flow shop scheduling problem has a wide application in the manufacturing and has attracted much attention in academic fields. From other point, on time delivery of products and services is a major necessity of companies’ todays; early and tardy delivery times will result additional cost such as holding or penalty costs.  In this paper, just-in-time (JIT) flow shop scheduling problem with preemption and machine idle time assumptions is considered in which objective function is minimizing the sum of weighted earliness and tardiness. A new non-linear mathematical model is formulated for this problem and due to high complexity of the problem meta-heuristic approaches have been applied to solve the problem for finding optimal solution. The parameters of algorithms are set by Taguchi method. Each parameter is tested in three levels. By implementation of many problems with different sizes these levels are determined .The proposed model is solved by three meta-heuristic algorithms: genetic algorithm (GA), imperialist competitive algorithm (ICA) and hybrid of GA and ICA. To evaluate the performance of the proposed algorithms many test problems have been designed. The Computational results indicate the superiority of the performance of hybrid approach than GA and ICA in finding thebest solution in reasonable computational time.

In recent decades, mathematical planning methods have been widely applied for optimization of decision-making processes under resource constraint conditions (Filip, 2017). The application of these methods has been emphasized in studies such as the allocation of land to various types of utilization in forest areas (Diaz & Romero, 2002), planning agricultural-forestry plans (de Sousa Xavier, 2015) and assessment of tourism development investment options (Carrillo et al., 2017). The present study applied a multi-objective programming method to optimize the level of investment and land allocation for the development of various types of tourism activities, considering three goals of increasing profit, decreasing erosion rate and increasing employment in Zara Park with an area of 73 hectares in Mazandaran Province. The required information was collected from area maps, relevant organizations and completed questionnaires from 120 visitors. The scale of erosion for each activity in a special area was determined with the use of affecting criteria from the FAO erosion assessment method and expert opinions for each activity in a special site. Limitations related to the physiological features of the area were considered through the creation of six homogeneous areas for the allocation of sports site, children's park, picnic, forest seeing and conservation. Other information was entered into the model as input data of objective function and constraint. By solving the model in Lingo11 mathematical programming software, a pay-off matrix was first created from separate optimization of goals, in order to assess the degree of conflict between them (Romero et al., 1987). Then, with the use of the weighting method, a series of efficient solutions were obtained. Finally, using the compromise programming method and creating a balance between the goals, the best answer among them was chosen. Agreed solutions were determined based on the preference of the decision makers in the weight of the goals. According to the results, on the condition of maximizing profits, annual revenue was estimated as 5.6 billion rails, the number of employees is 239 and the erosion rate was approximately 28. With a separate optimization minimizing erosion, the erosion was expected to decrease by 14 units, due to the modification in the area and the state of site assigned to each activity. In this case, a significant reduction of 3.1 billion rails in annual revenue and 19 people in employment can be envisaged. By maximizing the employment rate individually, the annual income was reduced to 3.4 billion. By simultaneously optimizing three goals in a multi-objective optimization approach, an efficient set of solutions was obtained in which the exchange between the goals could be observed. According to the results the change in the level of optimization of an objective affects the extent to which the other goals are achieved. From this set, optimal investment patterns were obtained with the use of a compromise programming method taking into consideration a different combination of objective weighting from the perspective of the three groups of park managers, tourism organizations and environmentalists. Based on these results, changing the weight of the goals significantly changed the amount of area and location allocated to each activity and the level of profitability. In the state of increasing the weight of the goal Profit, based on the preferences of park managers, the annual revenue will become nearer to the park estimation which is equal to 4.6 billion rails per year according to the comprehensive park studies and the sites allocated to each activity is approximately similar to the site expected by tourist’s point of view, as evaluated in the questionnaires. On an equal weighting, based on the preferences of the tourism authorities, although the level of income is lower than the estimated revenue, the other goals in this condition can come closer to their optimal level; ultimately, by considering environmentalist’s preferences, the percentage allocated to each land use changes in favour of the activities which are more compatible with the natural heritage, such as conservation and forest seeing. We face various and often conflicting goals in managing tourism resources, so multi-objective optimization methods integrated with compromise programming approaches which provide the possibility of exchange between the various preferences of managers and stakeholders can be used as an effective tool in facilitating the decision-making process.

Due to the increment of power demand, diminishing resources of fossil fuels and reduction of carbon dioxide out, the use of renewable energy resources is one of the important strategies in the developed countries. In this study, we use to meet electricity demand from renewable resources by as photovoltaic, batteries and wind turbines at times of peak production. The maximum amount of energy that solar panels and wind turbines can provide during any hour of the day is a function the amount of solar radiation and wind speed per hour. The aim is to minimize the cost of utilizing resources and maximize reliability. A non-linear programming model is proposed for the considered problem. Since the objectives are in conflict, for achieving a single optimal solution is not possible, therefore, NSGAII algorithm proposed for solving the problem

This paper considers the problem of minimizing make-span and maximum tardiness simultaneously for scheduling jobs under non-identical job sizes, dynamic job arrivals, incompatible job families,and sequence-dependentfamily setup time on the single batch- processor, where split size of jobs is allowed between batches. At first, a new Mixed Integer Linear Programming (MILP) model is proposed for this problem; then, it is solved by -constraint method.Since this problem is NP-hard, a bi-objective genetic algorithm (BOGA) is offered for real-sized problems. The efficiency of the proposed BOGA is evaluated to be comparedwith many test problemsby -constraint method based on performance measures. The results show that the proposed BOGAis found to be more efficient and faster than the -constraint method in generating Pareto fronts in most cases.

In this study, Two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities is investigated. At the first stage, m-1 parallel machines process parts of each jobs and at the second stage related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time, however the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is Np-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, Hybrid Genetic Algorithm (HGA) and Hybrid Particle swarm Optimization (HPSO), are proposed. These algorithms are hybrids version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by Taguchi method and are used to solve many numerical examples. Finally statistical analysis illustrates that performance of HPSO is better than HGA.

This research deals with a hybrid flow shop scheduling problem with parallel batching, machine eligibility, unrelated parallel machine, and different release dates to minimize the sum of the total weighted earliness and tardiness (ET) penalties. In parallel batching situation, it is supposed that number of machine in some stages are able to perform a certain number of jobs simultaneously. Firstly, with respect to the proposed problem a mixed integer linear programming model is developed. Since the problem is NP-hard, for solving large size problems, a hybrid meta-heuristic algorithm which combines artificial immune system and simulated annealing is proposed. The performance of hybrid algorithm is tested by some numerical experiments and the results show its superiority to the other two algorithms.

Most of the inventory control models assume that items can be stored indefinitely to meet the future demands. However, certain types of commodities either deteriorate or become obsolete in the course of time and hence are unstable. In this study, a mathematical model is presented for a two-echelon supply chain including a buyer and a producer for an inventory integrated system with non-instantaneous of items that demand is probable and follows a normal distribution. Since, the rate of deterioration describes the condition deterioration the goods and regarding the relation between time and deterioration rate is probable rather than the fixed rate of deterioration. In reality, considering the shortages is necessary in both forms of backlogging and lost sales. Therefore, both kinds of shortages are used in the model.
The main goal of this model is determining the optimal ordering policy so that the total cost of supply chain is minimized. The proposed model is solved for some problems by Lingo software. The validity of model is determined by sensitive analysis and the problem is known a NP-hard one, hence a genetic algorithm has been used in order to solve the model problem. The rates of deterioration and confidence level sensitivity analysis have been applied to analyze effect of some important parameters affecting on optimal solution of the inventory model.
Finally, the optimal value of the expected cost of supply chain under integrated and non-integrated decision-making has been determined and compared. The results show the efficiency of algorithm .

This paper deals with the school bus routing problem (SBRP). This problem is a variant of the vehicle routing problem where three simultaneous decisions that have to be made: determining the set of stops to visit, for each student which stop he should walk to and the latter case occurs when determining the routes visited with the chosen stops, so that the total traveled distance is minimized. In the standard VRP all stops to visit are given, but in school bus routing problem, is assumed that a set of potential stops is given, as well as a set of students that can walk to one or more of these potential stops. The school buses used to pick up the students and transport them to school have a finite capacity. Ant Colony Optimization (ACO) is a meta-heuristic for combinatorial optimization problems. In this paper, artificial ant colony is developed. its successful application to the SBRP and finds optimal or close-to- optimal solutions of large instances of the SBRP in very limited computing times is shown.

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing time of the already processed jobs on the corresponding machine, i.e., the setup time of a job is past- sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total completion time (called TC) is minimized. Since that the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on ant colony optimization algorithm (ACO) is proposed to solve the given problem. The performance of the presented model and the proposed algorithm is verified by a number of numerical experiments. The related results show that ant colony optimization algorithm is effective and viable approache to generate optimal⁄near optimal solutions within a reasonable amount of computational time.